Improved image quality with simultaneously reduced radiation exposure: Knowledge-based iterative model reconstruction algorithms for coronary CT angiography in a clinical setting

Background: The aim of this study was to assess the potential for radiation dose reduction using knowledge-based iterative model reconstruction (K-IMR) algorithms in combination with ultra-low dose body mass index (BMI)-adapted protocols in coronary CT angiography (coronary CTA). Methods: Forty patients undergoing clinically indicated coronary CIA were randomly assigned to two groups with BMI-adapted (I: <25.0 kg/m(2), <28.0 kg/m(2), III: <30.0 kg/m(2), IV: >= 30.0 kg/m(2)) low dose (LD, I: 100kV(p)/75 mAs, II: 100kV(p)/100 mAs, Ill: 100kV(p)/150 mAs, IV: 120kV(p)/150 mAs, n = 20) or ultra low dose (ULD, I: 100kV(p)/50 mAs, II: 100kVp/75 mAs, III: 100kVp/100 mAs, IV: 120kV(p)/100 mAs, n = 20) protocols. Prospectively-triggered coronary cm was performed using a 256-MDCT with the lowest reasonable scan length. Images were generated with filtered back projection (FBP), a noise-reducing hybrid iterative algorithm (iD, levels 2/5) and K-IMR using cardiac routine (CR) and cardiac sharp settings, levels 1-3. Results: Groups were comparable regarding anthropometric parameters, heart rate, and scan length. The use of ULD protocols resulted in a significant reduction of radiation exposure (0.7 (0.6-0.9) mSv vs. 1.1 (0.9-1.7) mSv; p < 0.02). Image quality was significantly better in the ULD group using K-IMR CR 1 compared to FBP, iD 2 and iD 5 in the LD group, resulting in fewer non-diagnostic coronary segments (2.4% vs. 11.6%, 9.2% and 6.1%; p < 0.05). Conclusions: The combination of K-IMR with BMI-adapted ULD protocols results in significant radiation dose savings while simultaneously improving image quality compared to LD protocols with FBP or hybrid iterative algorithms. Therefore, K-IMR allows for coronary CIA examinations with high diagnostic value and very low radiation exposure in clinical routine. (C) 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.